Abstract

Social network analysis is aimed at analyzing relationships between social network users. Such analysis aims at finding community detection, that is, group of closest people in a network. Usually, graph clustering techniques are used to identify groups. Here, we propose a computational geometric approach to analyze social network. A Voronoi diagram-based clustering algorithm is employed over embedded dataset in the Euclidean vector space to identify groups. Structure-preserving embedding technique is used to embed the social network dataset and learns a low-rank kernel matrix by means of a semi-definite program with linear constraints that captures the connectivity structure of the input graph.